Data Literacy ~ Data Science and Machine Learning
Data Science and Machine Learning
Includes the following:
Natural language processing: NLP is a way for computers to analyze, understand and derive meaning from human language in a smart and useful way. NLP is a subset of artificial intelligence (AI).
Examples: Sentiment analysis, speech-to-text recognition, automatic summarization and language translation.
Natural language generation: NLG automates the creation of language or content from data inputs.
Examples: Weather reports, form letters and financial reports.
Artificial intelligence: AI is a set of related technologies that seems to emulate human thinking and action by learning, coming to its own conclusions and enhancing human cognitive performance (also known as cognitive computing) or replacing people on execution of nonroutine tasks.
Machine learning: ML algorithms are composed of many technologies (such as deep learning, neural networks and natural-language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information inputs.
Example use cases: Autonomous vehicles; automatic speech recognition and generation; detecting novel concepts and abstractions.
Relevant Links
These links are typically articles defining data literacy that include this theme in their definition.
Alan D. Duncan, Donna Medeiros, Aron Clarke, Sally Parker, Gartner, 2021;, Gartner, 2019;